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In this course, you will:
- Learn to code using cloud Jupyter notebooks.
- Create an end-to-end real-world course project.
- Obtain a validated certificate of accomplishment.
Syllabus:
1. Introduction to Programming with Python
- First steps with Python & Jupyter notebooks
- Arithmetic, conditional & logical operators in Python
- Quick tour with Variables and common data types
2. Next Steps with Python
- Branching with if, elif, and else
- Iteration with while and for loops
- Write reusable code with Functions
- Scope of variables and exceptions
3. Python Basics Practice
- Solve word problems using variables & arithmetic operations
- Manipulate data types using methods & operators
- Use branching and iterations to translate ideas into code
- Explore the documentation and get help from the community
4. Numerical Computing with Numpy
- Going from Python lists to Numpy arrays
- Working with multi-dimensional arrays
- Array operations, slicing and broadcasting
- Working with CSV data files
5. Numpy Array Operations
- Explore the Numpy documentation website
- Demonstrate usage 5 numpy array operations
- Publish a Jupyter notebook with explanations
- Share your work with the course community
6. Analyzing Tabular Data with Pandas
- Reading and writing CSV data with Pandas
- Querying, filtering and sorting data frames
- Grouping and aggregation for data summarization
- Merging and joining data from multiple sources
7. Pandas Practice
- Create data frames from CSV files
- Query and index operations on data frames
- Group, merge and aggregate data frames
- Fix missing and invalid values in data
8. Visualization with Matplotlib and Seaborn
- Basic visualizations with Matplotlib
- Advanced visualizations with Seaborn
- Tips for customizing and styling charts
- Plotting images and grids of charts
9. Exploratory Data Analysis
- Find a real-world dataset of your choice online
- Use Numpy & Pandas to parse, clean & analyze data
- Use Matplotlib & Seaborn to create visualizations
- Ask and answer interesting questions about the data
10. Exploratory Data Analysis - A Case Study
- Finding a good real-world dataset for EDA
- Data loading, cleaning and preprocessing
- Exploratory analysis and visualization
- Answering questions and making inferences
